天下java本站尽知

超级管理员

35605

帖子

9

回复

54

积分

楼主
发表于 2020-01-03 07:00:06 | 查看: 191 | 回复: 0

前言

前情回顾

上一讲看了Eureka 注册中心的自我保护机制,以及里面提到的bug问题。

哈哈 转眼间都2020年了,这个系列的文章从12.17 一直写到现在,也是不容易哈,每天持续不断学习,输出博客,这一段时间确实收获很多。

今天在公司给组内成员分享了Eureka源码剖析,反响效果还可以,也算是感觉收获了点东西。后面还会继续feign、ribbon、hystrix的源码学习,依然文章连载的形式输出。

本讲目录

本讲主要是EurekaServer集群模式的数据同步讲解,主要目录如下。

目录如下:

  1. eureka server集群机制
  2. 注册、下线、续约的注册表同步机制
  3. 注册表同步三层队列机制详解

技术亮点:

  1. 3层队列机制实现注册表的批量同步需求

说明

原创不易,如若转载 请标明来源!

博客地址:一枝花算不算浪漫
微信公众号:壹枝花算不算浪漫

源码分析

eureka server集群机制

image.png

Eureka Server会在注册、下线、续约的时候进行数据同步,将信息同步到其他Eureka Server节点。

可以想象到的是,这里肯定不会是实时同步的,往后继续看注册表的同步机制吧。

注册、下线、续约的注册表同步机制

我们以Eureka Client注册为例,看看Eureka Server是如何同步给其他节点的。

PeerAwareInstanceRegistryImpl.java :

public void register(final InstanceInfo info, final boolean isReplication) {
    int leaseDuration = Lease.DEFAULT_DURATION_IN_SECS;
    if (info.getLeaseInfo() != null && info.getLeaseInfo().getDurationInSecs() > 0) {
        leaseDuration = info.getLeaseInfo().getDurationInSecs();
    }
    super.register(info, leaseDuration, isReplication);
    replicateToPeers(Action.Register, info.getAppName(), info.getId(), info, null, isReplication);
}

private void replicateToPeers(Action action, String appName, String id,
                                  InstanceInfo info /* optional */,
                                  InstanceStatus newStatus /* optional */, boolean isReplication) {
    Stopwatch tracer = action.getTimer().start();
    try {
        if (isReplication) {
            numberOfReplicationsLastMin.increment();
        }
        // If it is a replication already, do not replicate again as this will create a poison replication
        if (peerEurekaNodes == Collections.EMPTY_LIST || isReplication) {
            return;
        }

        for (final PeerEurekaNode node : peerEurekaNodes.getPeerEurekaNodes()) {
            // If the url represents this host, do not replicate to yourself.
            if (peerEurekaNodes.isThisMyUrl(node.getServiceUrl())) {
                continue;
            }
            replicateInstanceActionsToPeers(action, appName, id, info, newStatus, node);
        }
    } finally {
        tracer.stop();
    }
}

private void replicateInstanceActionsToPeers(Action action, String appName,
                                                 String id, InstanceInfo info, InstanceStatus newStatus,
                                                 PeerEurekaNode node) {
    try {
        InstanceInfo infoFromRegistry = null;
        CurrentRequestVersion.set(Version.V2);
        switch (action) {
            case Cancel:
                node.cancel(appName, id);
                break;
            case Heartbeat:
                InstanceStatus overriddenStatus = overriddenInstanceStatusMap.get(id);
                infoFromRegistry = getInstanceByAppAndId(appName, id, false);
                node.heartbeat(appName, id, infoFromRegistry, overriddenStatus, false);
                break;
            case Register:
                node.register(info);
                break;
            case StatusUpdate:
                infoFromRegistry = getInstanceByAppAndId(appName, id, false);
                node.statusUpdate(appName, id, newStatus, infoFromRegistry);
                break;
            case DeleteStatusOverride:
                infoFromRegistry = getInstanceByAppAndId(appName, id, false);
                node.deleteStatusOverride(appName, id, infoFromRegistry);
                break;
        }
    } catch (Throwable t) {
        logger.error("Cannot replicate information to {} for action {}", node.getServiceUrl(), action.name(), t);
    }
}
  1. 注册完成后,调用replicateToPeers(),注意这里面有一个参数isReplication,如果是true,代表是其他Eureka Server节点同步的,false则是EurekaClient注册来的。
  2. replicateToPeers()中一段逻辑,如果isReplication为true则直接跳出,这里意思是client注册来的服务实例需要向其他节点扩散,如果不是则不需要去同步
  3. peerEurekaNodes.getPeerEurekaNodes()拿到所有的Eureka Server节点,循环遍历去同步数据,调用replicateInstanceActionsToPeers()
  4. replicateInstanceActionsToPeers()方法中根据注册、下线、续约等去处理不同逻辑

接下来就是真正执行同步逻辑的地方,这里主要用了三层队列对同步请求进行了batch操作,将请求打成一批批 然后向各个EurekaServer进行http请求。

注册表同步三层队列机制详解

到了这里就是真正进入了同步的逻辑,这里还是以上面注册逻辑为主线,接着上述代码继续往下跟:

PeerEurekaNode.java :

public void register(final InstanceInfo info) throws Exception {
    long expiryTime = System.currentTimeMillis() + getLeaseRenewalOf(info);
    batchingDispatcher.process(
            taskId("register", info),
            new InstanceReplicationTask(targetHost, Action.Register, info, null, true) {
                public EurekaHttpResponse<Void> execute() {
                    return replicationClient.register(info);
                }
            },
            expiryTime
    );
}

这里会执行batchingDispatcher.process() 方法,我们继续点进去,然后会进入 TaskDispatchers.createBatchingTaskDispatcher() 方法,查看其中的匿名内部类中的process()方法:

void process(ID id, T task, long expiryTime) {
        // 将请求都放入到acceptorQueue中
        acceptorQueue.add(new TaskHolder<ID, T>(id, task, expiryTime));
        acceptedTasks++;
    }

将需要同步的Task数据放入到acceptorQueue队列中。
接着回到createBatchingTaskDispatcher()方法中,看下AcceptorExecutor,它的构造函数中会启动一个后台线程:

ThreadGroup threadGroup = new ThreadGroup("eurekaTaskExecutors");

this.acceptorThread = new Thread(threadGroup, new AcceptorRunner(), "TaskAcceptor-" + id);

我们继续跟AcceptorRunner.java:

class AcceptorRunner implements Runnable {
    @Override
    public void run() {
        long scheduleTime = 0;
        while (!isShutdown.get()) {
            try {
                // 处理acceptorQueue队列中的数据
                drainInputQueues();

                int totalItems = processingOrder.size();

                long now = System.currentTimeMillis();
                if (scheduleTime < now) {
                    scheduleTime = now + trafficShaper.transmissionDelay();
                }
                if (scheduleTime <= now) {
                    // 将processingOrder拆分成一个个batch,然后进行操作
                    assignBatchWork();
                    assignSingleItemWork();
                }

                // If no worker is requesting data or there is a delay injected by the traffic shaper,
                // sleep for some time to avoid tight loop.
                if (totalItems == processingOrder.size()) {
                    Thread.sleep(10);
                }
            } catch (InterruptedException ex) {
                // Ignore
            } catch (Throwable e) {
                // Safe-guard, so we never exit this loop in an uncontrolled way.
                logger.warn("Discovery AcceptorThread error", e);
            }
        }
    }

    private void drainInputQueues() throws InterruptedException {
        do {
            drainAcceptorQueue();

            if (!isShutdown.get()) {
                // If all queues are empty, block for a while on the acceptor queue
                if (reprocessQueue.isEmpty() && acceptorQueue.isEmpty() && pendingTasks.isEmpty()) {
                    TaskHolder<ID, T> taskHolder = acceptorQueue.poll(10, TimeUnit.MILLISECONDS);
                    if (taskHolder != null) {
                        appendTaskHolder(taskHolder);
                    }
                }
            }
        } while (!reprocessQueue.isEmpty() || !acceptorQueue.isEmpty() || pendingTasks.isEmpty());
    }

    private void drainAcceptorQueue() {
        while (!acceptorQueue.isEmpty()) {
            // 将acceptor队列中的数据放入到processingOrder队列中去,方便后续拆分成batch
            appendTaskHolder(acceptorQueue.poll());
        }
    }

    private void appendTaskHolder(TaskHolder<ID, T> taskHolder) {
        if (isFull()) {
            pendingTasks.remove(processingOrder.poll());
            queueOverflows++;
        }
        TaskHolder<ID, T> previousTask = pendingTasks.put(taskHolder.getId(), taskHolder);
        if (previousTask == null) {
            processingOrder.add(taskHolder.getId());
        } else {
            overriddenTasks++;
        }
    }
            
}

认真跟这里面的代码,可以看到这里是将上面的acceptorQueue放入到processingOrder, 其中processingOrder也是一个队列。

AcceptorRunner.javarun()方法中,还会调用assignBatchWork()方法,这里面就是将processingOrder打成一个个batch,接着看代码:

void assignBatchWork() {
            if (hasEnoughTasksForNextBatch()) {
                if (batchWorkRequests.tryAcquire(1)) {
                    long now = System.currentTimeMillis();
                    int len = Math.min(maxBatchingSize, processingOrder.size());
                    List<TaskHolder<ID, T>> holders = new ArrayList<>(len);
                    while (holders.size() < len && !processingOrder.isEmpty()) {
                        ID id = processingOrder.poll();
                        TaskHolder<ID, T> holder = pendingTasks.remove(id);
                        if (holder.getExpiryTime() > now) {
                            holders.add(holder);
                        } else {
                            expiredTasks++;
                        }
                    }
                    if (holders.isEmpty()) {
                        batchWorkRequests.release();
                    } else {
                        batchSizeMetric.record(holders.size(), TimeUnit.MILLISECONDS);
                        // 将批量数据放入到batchWorkQueue中
                        batchWorkQueue.add(holders);
                    }
                }
            }
        }

        private boolean hasEnoughTasksForNextBatch() {
            if (processingOrder.isEmpty()) {
                return false;
            }
            // 默认maxBufferSize为250
            if (pendingTasks.size() >= maxBufferSize) {
                return true;
            }

            TaskHolder<ID, T> nextHolder = pendingTasks.get(processingOrder.peek());
            // 默认maxBatchingDelay为500ms
            long delay = System.currentTimeMillis() - nextHolder.getSubmitTimestamp();
            return delay >= maxBatchingDelay;
        }

这里加入batch的规则是:maxBufferSize 默认为250
maxBatchingDelay 默认为500ms,打成一个个batch后就开始发送给server端。至于怎么发送 我们接着看 PeerEurekaNode.java, 我们在最开始调用register()方法就是调用PeerEurekaNode.register(), 我们来看看它的构造方法:

PeerEurekaNode(PeerAwareInstanceRegistry registry, String targetHost, String serviceUrl,
                                     HttpReplicationClient replicationClient, EurekaServerConfig config,
                                     int batchSize, long maxBatchingDelayMs,
                                     long retrySleepTimeMs, long serverUnavailableSleepTimeMs) {
    this.registry = registry;
    this.targetHost = targetHost;
    this.replicationClient = replicationClient;

    this.serviceUrl = serviceUrl;
    this.config = config;
    this.maxProcessingDelayMs = config.getMaxTimeForReplication();

    String batcherName = getBatcherName();
    ReplicationTaskProcessor taskProcessor = new ReplicationTaskProcessor(targetHost, replicationClient);
    this.batchingDispatcher = TaskDispatchers.createBatchingTaskDispatcher(
            batcherName,
            config.getMaxElementsInPeerReplicationPool(),
            batchSize,
            config.getMaxThreadsForPeerReplication(),
            maxBatchingDelayMs,
            serverUnavailableSleepTimeMs,
            retrySleepTimeMs,
            taskProcessor
    );
}

这里会实例化一个ReplicationTaskProcessor.java, 我们跟进去,发下它是实现TaskProcessor的,所以一定会执行此类中的process()方法,执行方法如下:

public ProcessingResult process(List<ReplicationTask> tasks) {
    ReplicationList list = createReplicationListOf(tasks);
    try {
        EurekaHttpResponse<ReplicationListResponse> response = replicationClient.submitBatchUpdates(list);
        int statusCode = response.getStatusCode();
        if (!isSuccess(statusCode)) {
            if (statusCode == 503) {
                logger.warn("Server busy (503) HTTP status code received from the peer {}; rescheduling tasks after delay", peerId);
                return ProcessingResult.Congestion;
            } else {
                // Unexpected error returned from the server. This should ideally never happen.
                logger.error("Batch update failure with HTTP status code {}; discarding {} replication tasks", statusCode, tasks.size());
                return ProcessingResult.PermanentError;
            }
        } else {
            handleBatchResponse(tasks, response.getEntity().getResponseList());
        }
    } catch (Throwable e) {
        if (isNetworkConnectException(e)) {
            logNetworkErrorSample(null, e);
            return ProcessingResult.TransientError;
        } else {
            logger.error("Not re-trying this exception because it does not seem to be a network exception", e);
            return ProcessingResult.PermanentError;
        }
    }
    return ProcessingResult.Success;
}

这里面是将List<ReplicationTask> tasks 通过submitBatchUpdate() 发送给server端。
server端在PeerReplicationResource.batchReplication()去处理,实际上就是循环调用ApplicationResource.addInstance() 方法,又回到了最开始注册的方法。

到此 EurekaServer同步的逻辑就结束了,这里主要是三层队列的数据结构很绕,通过一个batchList去批量同步数据的。

注意这里还有一个很重要的点,就是Client注册时调用addInstance()方法,这里到了server端PeerAwareInstanceRegistryImpl会执行同步其他EurekaServer逻辑。

而EurekaServer同步注册接口仍然会调用addInstance()方法,这里难不成就死循环调用了?当然不是,addInstance()中也有个参数:isReplication, 在最后调用server端方法的时候如下:registry.register(info, "true".equals(isReplication));

我们知道,EurekaClient在注册的时候isReplication传递为空,所以这里为false,而Server端同步的时候调用:

PeerReplicationResource:

private static Builder handleRegister(ReplicationInstance instanceInfo, ApplicationResource applicationResource) {
        applicationResource.addInstance(instanceInfo.getInstanceInfo(), REPLICATION);
        return new Builder().setStatusCode(Status.OK.getStatusCode());
    }

这里的REPLICATION 为true

另外在AbstractJersey2EurekaHttpClient中发送register请求的时候,有个addExtraHeaders()方法,如下图:

image.png

如果是使用的Jersey2ReplicationClient发送的,那么header中的x-netflix-discovery-replication配置则为true,在后面执行注册的addInstance()方法中会接收这个参数的:

总结

仍然一图流,文中解析的内容都包含在这张图中了:

11_Eureka注册中心集群同步原理.png

申明

本文章首发自本人博客:https://www.cnblogs.com/wang-meng 和公众号:壹枝花算不算浪漫,如若转载请标明来源!

感兴趣的小伙伴可关注个人公众号:壹枝花算不算浪漫

22.jpg


您需要登录后才可以回帖 登录 | 立即注册